Tag Archives: predictive modelling

Training of a neural network on large datasets could be a rather long and challenging thing. There are lots of approaches for reducing training time: parallelization, early stopping, momentum, dimensionality reduction etc. They provide faster convergence of training, prevent unnecessary iterations, utilize hardware resources in a more efficient way. In this post we'll see how good initialization can affect training. Continue reading PCA-based pretraining of neural networks→

In the first part of this post we considered a human-based binary random numbers generator and built a predictive model for it. The model appeared to work better than a coin toss, at least for a short sequence we had. This part develops more complex models with somewhat higher accuracy. Continue reading Prediction of random numbers. Part 2→